




"""simple docstring"""







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image

from diffusers import (
    DDIMScheduler,
    KandinskyVaaInpaintPipeline,
    KandinskyVaaPriorPipeline,
    UNetaDConditionModel,
    VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference


enable_full_determinism()

class 				snake_case_			(					_UpperCAmelCase	, unittest.TestCase			):







   """simple docstring"""




   A_				      =       KandinskyVaaInpaintPipeline
   A_				      =       ['''image_embeds''', '''negative_image_embeds''', '''image''', '''mask_image''']
   A_				      =       [
       '''image_embeds''',
       '''negative_image_embeds''',
       '''image''',
       '''mask_image''',
   ]
   A_				      =       [
       '''generator''',
       '''height''',
       '''width''',
       '''latents''',
       '''guidance_scale''',
       '''num_inference_steps''',
       '''return_dict''',
       '''guidance_scale''',
       '''num_images_per_prompt''',
       '''output_type''',
       '''return_dict''',
   ]
   A_				      =       False







   @property
   def   UpperCAmelCase__			(		self)				->   Union[str, Any]:
       return 3_2







   @property
   def   UpperCAmelCase__			(		self)				->   Optional[Any]:
       return 3_2







   @property
   def   UpperCAmelCase__			(		self)				->   Tuple:
       return self.time_input_dim







   @property
   def   UpperCAmelCase__			(		self)				->   Dict:
       return self.time_input_dim * 4







   @property
   def   UpperCAmelCase__			(		self)				->   int:
       return 1_0_0







   @property
   def   UpperCAmelCase__			(		self)				->   Optional[int]:
       torch.manual_seed(0)

       UpperCamelCase              =	{
           '''in_channels''': 9,
           # Out channels is double in channels because predicts mean and variance
           '''out_channels''': 8,
           '''addition_embed_type''': '''image''',
           '''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
           '''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
           '''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
           '''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
           '''layers_per_block''': 1,
           '''encoder_hid_dim''': self.text_embedder_hidden_size,
           '''encoder_hid_dim_type''': '''image_proj''',
           '''cross_attention_dim''': self.cross_attention_dim,
           '''attention_head_dim''': 4,
           '''resnet_time_scale_shift''': '''scale_shift''',
           '''class_embed_type''': None,
       }

       UpperCamelCase              =	UNetaDConditionModel(**lowerCamelCase_)
       return model







   @property
   def   UpperCAmelCase__			(		self)				->   Optional[Any]:
       return {
           "block_out_channels": [3_2, 6_4],
           "down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
           "in_channels": 3,
           "latent_channels": 4,
           "layers_per_block": 1,
           "norm_num_groups": 8,
           "norm_type": "spatial",
           "num_vq_embeddings": 1_2,
           "out_channels": 3,
           "up_block_types": [
               "AttnUpDecoderBlock2D",
               "UpDecoderBlock2D",
           ],
           "vq_embed_dim": 4,
       }







   @property
   def   UpperCAmelCase__			(		self)				->   List[Any]:
       torch.manual_seed(0)
       UpperCamelCase              =	VQModel(**self.dummy_movq_kwargs)
       return model







   def   UpperCAmelCase__			(		self)				->   Dict:
       UpperCamelCase              =	self.dummy_unet
       UpperCamelCase              =	self.dummy_movq

       UpperCamelCase              =	DDIMScheduler(
           num_train_timesteps=1_0_0_0       ,					beta_schedule='''linear'''       ,					beta_start=0.0_0085       ,					beta_end=0.012       ,					clip_sample=lowerCamelCase_       ,					set_alpha_to_one=lowerCamelCase_       ,					steps_offset=1       ,					prediction_type='''epsilon'''       ,					thresholding=lowerCamelCase_       ,					)

       UpperCamelCase              =	{
           '''unet''': unet,
           '''scheduler''': scheduler,
           '''movq''': movq,
       }

       return components







   def   UpperCAmelCase__			(		self       ,					lowerCamelCase_       ,					lowerCamelCase_=0)				->   List[str]:
       UpperCamelCase              =	floats_tensor((1, self.text_embedder_hidden_size)       ,					rng=random.Random(lowerCamelCase_)).to(lowerCamelCase_)
       UpperCamelCase              =	floats_tensor((1, self.text_embedder_hidden_size)       ,					rng=random.Random(seed + 1)).to(
           lowerCamelCase_)
       # create init_image
       UpperCamelCase              =	floats_tensor((1, 3, 6_4, 6_4)       ,					rng=random.Random(lowerCamelCase_)).to(lowerCamelCase_)
       UpperCamelCase              =	image.cpu().permute(0       ,					2       ,					3       ,					1)[0]
       UpperCamelCase              =	Image.fromarray(np.uinta(lowerCamelCase_)).convert('''RGB''').resize((2_5_6, 2_5_6))
       # create mask
       UpperCamelCase              =	np.ones((6_4, 6_4)       ,					dtype=np.floataa)
       UpperCamelCase              =	0

       if str(lowerCamelCase_).startswith('''mps'''):
           UpperCamelCase              =	torch.manual_seed(lowerCamelCase_)
       else:
           UpperCamelCase              =	torch.Generator(device=lowerCamelCase_).manual_seed(lowerCamelCase_)
       UpperCamelCase              =	{
           '''image''': init_image,
           '''mask_image''': mask,
           '''image_embeds''': image_embeds,
           '''negative_image_embeds''': negative_image_embeds,
           '''generator''': generator,
           '''height''': 6_4,
           '''width''': 6_4,
           '''num_inference_steps''': 2,
           '''guidance_scale''': 4.0,
           '''output_type''': '''np''',
       }
       return inputs







   def   UpperCAmelCase__			(		self)				->   Any:
       UpperCamelCase              =	'''cpu'''

       UpperCamelCase              =	self.get_dummy_components()

       UpperCamelCase              =	self.pipeline_class(**lowerCamelCase_)
       UpperCamelCase              =	pipe.to(lowerCamelCase_)

       pipe.set_progress_bar_config(disable=lowerCamelCase_)

       UpperCamelCase              =	pipe(**self.get_dummy_inputs(lowerCamelCase_))
       UpperCamelCase              =	output.images

       UpperCamelCase              =	pipe(
           **self.get_dummy_inputs(lowerCamelCase_)       ,					return_dict=lowerCamelCase_       ,					)[0]

       UpperCamelCase              =	image[0, -3:, -3:, -1]
       UpperCamelCase              =	image_from_tuple[0, -3:, -3:, -1]

       print(F'image.shape {image.shape}')

       assert image.shape == (1, 6_4, 6_4, 3)

       UpperCamelCase              =	np.array(
           [0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848])

       assert (
           np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
       ), F' expected_slice {expected_slice}, but got {image_slice.flatten()}'
       assert (
           np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
       ), F' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'







   def   UpperCAmelCase__			(		self)				->   Any:
       super().test_inference_batch_single_identical(expected_max_diff=3e-3)

@slow
@require_torch_gpu
class 				snake_case_			(					unittest.TestCase			):







   """simple docstring"""




   def   UpperCAmelCase__			(		self)				->   Any:
       # clean up the VRAM after each test
       super().tearDown()
       gc.collect()
       torch.cuda.empty_cache()







   def   UpperCAmelCase__			(		self)				->   Dict:
       UpperCamelCase              =	load_numpy(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
           '''/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy''')

       UpperCamelCase              =	load_image(
           '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/kandinsky/cat.png''')
       UpperCamelCase              =	np.ones((7_6_8, 7_6_8)       ,					dtype=np.floataa)
       UpperCamelCase              =	0

       UpperCamelCase              =	'''a hat'''

       UpperCamelCase              =	KandinskyVaaPriorPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-prior'''       ,					torch_dtype=torch.floataa)
       pipe_prior.to(lowerCamelCase_)

       UpperCamelCase              =	KandinskyVaaInpaintPipeline.from_pretrained(
           '''kandinsky-community/kandinsky-2-2-decoder-inpaint'''       ,					torch_dtype=torch.floataa)
       UpperCamelCase              =	pipeline.to(lowerCamelCase_)
       pipeline.set_progress_bar_config(disable=lowerCamelCase_)

       UpperCamelCase              =	torch.Generator(device='''cpu''').manual_seed(0)
       UpperCamelCase       ,	UpperCamelCase              =	pipe_prior(
           lowerCamelCase_       ,					generator=lowerCamelCase_       ,					num_inference_steps=5       ,					negative_prompt=''''''       ,					).to_tuple()

       UpperCamelCase              =	pipeline(
           image=lowerCamelCase_       ,					mask_image=lowerCamelCase_       ,					image_embeds=lowerCamelCase_       ,					negative_image_embeds=lowerCamelCase_       ,					generator=lowerCamelCase_       ,					num_inference_steps=1_0_0       ,					height=7_6_8       ,					width=7_6_8       ,					output_type='''np'''       ,					)

       UpperCamelCase              =	output.images[0]

       assert image.shape == (7_6_8, 7_6_8, 3)

       assert_mean_pixel_difference(lowerCamelCase_       ,					lowerCamelCase_)






"""simple docstring"""





from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)






